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1. A large set of texts used to train a model to understand and generate language.
2. The process of breaking text into smaller pieces, called tokens, which can be words or subwords.
3. A step-by-step procedure for solving a problem or accomplishing a task, often used in artificial intelligence to guide machine learning.
4. The simulation of human intelligence processes by computer systems.
5. A type of artificial intelligence that uses a knowledge base and inference rules to solve complex problems in a specialized domain.
6. The reduction in expenses or overhead through the implementation of AI.
7. A type of machine learning algorithm that uses principles of evolution to generate solutions to complex problems.
8. As AI becomes more integrated into various domains, there is a risk of over-reliance and reduced human autonomy.
9. Vehicles that are capable of operating without human intervention, using sensors and machine learning algorithms to navigate and make decisions.
10. AI systems can be vulnerable to hacking and manipulation, leading to potential security breaches and misuse of information.
11. Where a text is represented as an unordered collection of words.
12. The process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
13. Transparency refers to the ability to clearly understand and interpret the inner workings and decision-making processes of an AI system.
14. The process of using a trained model to generate predictions or outputs based on new input data.
15. Machine learning software that acts autonomously on a user's behalf.
16. A method of further training a pre-trained model on a specific dataset to improve performance on a particular task.
17. A type of machine learning algorithm that uses layers of neural networks to learn and make predictions from complex data sets.
18. A type of artificial intelligence that attempts to simulate human thought processes and decision-making.